Triple
T13201614
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chamartín |
E314254
|
entity |
| Predicate | borderedBy |
P224
|
FINISHED |
| Object | Tetuán |
E639934
|
NE FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Tetuán | Statement: [Chamartín, borderedBy, Tetuán]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tetuán Context triple: [Chamartín, borderedBy, Tetuán]
-
A.
Tetuán
chosen
Tetuán is a station on the Madrid Metro network serving the Tetuán district in the north of Spain’s capital.
-
B.
Tetuan
Tetuan is a Barcelona Metro station on line 2 located beneath Plaça de Tetuan in the Eixample district of Barcelona, Spain.
-
C.
El Azbakeya
El Azbakeya is a historic district in central Cairo known for its cultural landmarks, markets, and longstanding role as an urban hub of the city.
-
D.
El Ejido
El Ejido is a major agricultural town in southeastern Spain, renowned for its extensive greenhouse farming and intensive fruit and vegetable production.
-
E.
Xàtiva
Xàtiva is a historic town in the Valencian Community of Spain, known for its medieval castle, rich cultural heritage, and role as the birthplace of the Borgia family.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69d806aee7308190b70a237ba2a6e3e1 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d98c6591d881909a6ebc22246caead |
completed | April 10, 2026, 11:48 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6f60cf610819098d0c45989e3f9cf |
completed | May 3, 2026, 7:15 a.m. |
Created at: April 9, 2026, 9:16 p.m.